Compressive Sensing for Polyharmonic Subdivision Wavelets With Applications to Image Analysis
This work offers a new wavelet family for compressive sensing in image analysis, but the improvements are incremental and domain-specific.
The authors apply compressive sensing with polyharmonic subdivision wavelets to image analysis, achieving efficient image recovery from fewer samples than required by the Nyquist-Shannon theorem. Experiments on Lena and astronomical images demonstrate improved performance over Daubechies wavelets.
We apply successfully the Compressive Sensing approach for Image Analysis using the new family of Polyharmonic Subdivision wavelets. We show that this approach provides a very efficient recovery of the images based on fewer samples than the traditional Shannon-Nyquist paradigm. We provide the results of experiments with PHSD wavelets and Daubechies wavelets, for the Lena image and astronomical images.